Alignment of Continuous Video onto 3D Point Clouds and Automatic Registration and Visualization of Occluded Targets Using Ladar Data

Alignment of Continuous Video onto 3D Point Clouds
W. Zhao, D. Nister, S. Hsu
We propose a general framework for aligning continuous (oblique) video
onto 3D sensor data. We align a point cloud computed from the video
onto the point cloud directly obtained from a 3D sensor. This is in contrast
to existing techniques where the 2D images are aligned to a 3D model
derived from the 3D sensor data. Using point clouds enables the
alignment for scenes full of objects that are difficult to model, for
example, trees. To compute 3D point clouds from video, motion stereo is
used along with a state-of-the-art algorithm for camera pose
estimation. Our experiments with real data demonstrate the advantages of the
proposed registration algorithm for texturing models in large-scale
semi-urban environments.
The capability to align video before a 3D model is built from the 3D
sensor data opens up new possibilities for 3D modeling. We introduce a
novel modeling-through-registration approach that fuses 3D information
from both the 3D sensor and the video. Initial experiments with real
data illustrate the potential of the proposed approach.

Automatic R egistration and Visualization of Occluded Targets Using Ladar Data
S. Hsu, S. Samarasekera, R. Kumar
High-resolution 3D imaging ladar systems can penetrate foliage and
camouflage to sample fragments of concealed surfaces of interest.
Samples collected while the ladar moves can be integrated into a
coherent object shape, provided that sensor poses are known. We detail
a system for automatic data-driven registration of ladar frames,
consisting of a coarse search stage, a pairwise fine registration stage
using an iterated closest points algorithm, and a multi-view
registration strategy. We evaluate this approach using simulated and
field-collected ladar imagery of foliage-occluded objects. Even after
alignment and aggregation, it is often difficult for human observers to
find, assess, and recognize objects from a point cloud display. We
survey and demonstrate basic display manipulations, surface fitting
techniques, and clutter suppression to enhance visual exploitation of
3D imaging ladar data.